The Bayesian Learning Rule for Adaptive AI

Abstract: Humans and animals have a natural ability to autonomously learn and quickly adapt to their surroundings. How can we design AI systems that do the same? In this talk, I will present Bayesian principles to bridge such gaps between humans and AI. I will show that a wide variety of machine-learning algorithms are instances of a single learning-rule called the Bayesian learning rule. The rule unravels a dual perspective yielding new adaptive mechanisms for machine-learning based AI systems. My hope is to convince the audience that Bayesian principles are indispensable for an AI that learns as efficiently as we do.

Reference: M.E. Khan, H. Rue, The Bayesian Learning Rule [arXiv] [Tweet]

data structures and algorithmsmachine learningmathematical physicsinformation theoryoptimization and controldata analysis, statistics and probability

Audience: researchers in the topic

Mathematics, Physics and Machine Learning (IST, Lisbon)

Series comments: To receive the series announcements, please register in:
mpml.tecnico.ulisboa.pt
mpml.tecnico.ulisboa.pt/registration
Zoom link: videoconf-colibri.zoom.us/j/91599759679